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Practical data analysis /
Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis. Practical Data Analysis is a book ideal for home and small business users who want...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2013.
|
| Colección: | Community experience distilled
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Cuesta, Hector. | |
| 245 | 1 | 0 | |a Practical data analysis / |c Hector Cuesta. |
| 260 | |a Birmingham, UK : |b Packt Publishing, |c 2013. | ||
| 300 | |a 1 online resource (360 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 0 | |a Community experience distilled | |
| 588 | 0 | |a Print version record. | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1:Getting Started; Computer science; Artificial intelligence (AI); Machine Learning (ML); Statistics; Mathematics; Knowledge domain; Data, information, and knowledge; The nature of data; The data analysis process; The problem; Data preparation; Data exploration; Predictive modeling; Visualization of results; Quantitative versus qualitative data analysis; Importance of data visualization; What about big data?; Sensors and cameras. | |
| 505 | 8 | |a Social networks analysisTools and toys for this book; Why Python?; Why mlpy?; Why D3.js?; Why MongoDB?; Summary; Chapter 2:Working with Data; Data sources; Open data; Text files; Excel files; SQL databases; NoSQL databases; Multimedia; Web scraping; Data scrubbing; Statistical methods; Text parsing; Data transformation; Data formats; CSV; Parsing a CSV file with the csv module; Parsing a CSV file using NumPy; JSON; Parsing a JSON file using json module; XML; Parsing an XML file in Python using xml module; YAML; Getting started with OpenRefine; Text facet; Clustering; Text filters. | |
| 505 | 8 | |a Numeric facetsTransforming data; Exporting data; Operation history; Summary; Chapter 3:Data Visualization; Data-Driven Documents (D3); HTML; DOM; CSS; JavaScript; SVG; Getting started with D3.js; Bar chart; Pie chart; Scatter plot; Single line chart; Multi-line chart; Interaction and animation; Summary; Chapter 4:Text Classification; Learning and classification; Bayesian classification; Naïve Bayes algorithm; E-mail subject line tester; The algorithm; Classifier accuracy; Summary; Chapter 5:Similarity-based Image Retrieval; Image similarity search; Dynamic time warping (DTW). | |
| 505 | 8 | |a Processing the image datasetImplementing DTW; Analyzing the results; Summary; Chapter 6:Simulation of Stock Prices; Financial time series; Random walk simulation; Monte Carlo methods; Generating random numbers; Implementation in D3.js; Summary; Chapter 7:Predicting Gold Prices; Working with the time series data; Components of a time series; Smoothing the time series; The data -- historical gold prices; Nonlinear regression; Kernel ridge regression; Smoothing the gold prices time series; Predicting in the smoothed time series; Contrasting the predicted value; Summary. | |
| 505 | 8 | |a Chapter 8:Working with Support Vector MachinesUnderstanding the multivariate dataset; Dimensionality reduction; Linear Discriminant Analysis; Principal Component Analysis; Getting started with support vector machine; Kernel functions; Double spiral problem; SVM implemented on mlpy; Summary; Chapter 9:Modeling Infectious Disease with Cellular Automata; Introduction to epidemiology; The epidemiology triangle; The epidemic models; The SIR model; Solving ordinary differential equation for the SIR model with SciPy; The SIRS model; Modelling with cellular automata cell, state, grid, and neighborhood. | |
| 520 | |a Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis. Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle. | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a Knovel |b ACADEMIC - Software Engineering | ||
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a Databases. | |
| 650 | 0 | |a Data structures (Computer science) | |
| 650 | 0 | |a System design. | |
| 650 | 0 | |a System analysis. | |
| 650 | 6 | |a Structures de données (Informatique) | |
| 650 | 6 | |a Conception de systèmes. | |
| 650 | 6 | |a Analyse de systèmes. | |
| 650 | 7 | |a systems analysis. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Databases |x General. |2 bisacsh | |
| 650 | 7 | |a Electronic data processing |2 fast | |
| 650 | 7 | |a Data structures (Computer science) |2 fast | |
| 650 | 7 | |a Databases |2 fast | |
| 650 | 7 | |a System analysis |2 fast | |
| 650 | 7 | |a System design |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Cuesta, Hector. |t Practical Data Analysis. |d Birmingham : Packt Publishing, ©2013 |z 9781783280995 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1507840 |z Texto completo |
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